Justin ("Goju") Gottschlich
Founder, CEO & Chief Scientist @ Merly Inc.
Adjunct Lecturer @ Stanford University
Steering Committee Chair, ACM SIGPLAN Machine Programming Symposium (MAPS)
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Highlights
Highlights
Merly is our machine programming AI start-up. Merly's website: http://merly.ai
Our Machine Programming & Technology YouTube Channel (subscribe & stay updated)
Keynote at LADSIOS (co-located with VLDB '21): "Machine Programming and the Future of Software Development"
New demo of one of our production quality MP systems: AutoPerf
Our team, joint w/ MIT & Microsoft, won two awards at SIGMOD '21!
Keynote @ MIT's DSAIL 2021 virtual retreat: "A Glimpse Into Machine Programming @ Intel Labs"
Invited talk @ UWisc's 2020 MLOS Seminar Series: "Machine Programming: Challenges and Opportunities" (video)
Keynote @ Penn's PRECISE 2019 Industry Day: "Machine Programming: The Future of Autonomy"
Our research has been highlighted by venues like Wall Street Journal, DeepLearning.ai, Communications of the ACM, New York Times, SDTimes, Economic Times, Venturebeat, and Wharton, and many others.
Recent Committees
Recent Committees
NeurIPS '22, ICLR'22, PLDI'22, CGO'21, NeurIPS'21, AIDB'21, PACT'21, FSE'21, OOPSLA'21, MAPS'21 (SC chair), ICML'21, USENIX ATC'21, ICLR'21, MLSys'21, NeurIPS'20, MAPL'20 (SC chair), JPDC'20, aiDM '20, TheWebConf'20, MLSys'20, PACT'19 (SRC), SysML'19, MAPL'18 (general chair)
Contact: justin.gottschlich@merly.ai
Brief Biographical Sketch
Brief Biographical Sketch
In 2022, I founded my second startup, Merly, where we aim to build machine programming systems to help create better software, faster. I created my first tech startup when I was 25, where I wrote a few million lines of C++ code building the online game, Nodeka. At Merly, I serve both as chief executive officer (CEO) and chief scientist. Merly is principally interested in (i) improving the rate at which we develop software while concurrently (ii) improving its quality. We achieve this by employing a variety of automation techniques -- otherwise known as machine programming -- such as deep neural networks and formal methods. Learn more here (https://merly.ai).
Previously, I founded and led the Machine Programming Research group at Intel Labs. Machine programming (MP) is a new field of research that uses automation to improve the rate at which we develop software (e.g., the time it takes a developer to write, maintain, and test code) and improve its associated quality characteristics (e.g., performance, correctness, security, maintainability, etc.). We generally consider MP as a fusion of four domains: (i) machine learning, (ii) programming languages, (iii) software engineering and (iv) computing systems. We provide a brief overview of MP in our “Three Pillars of Machine Programming” vision paper (see my Stanford MP course page or MIT's Armando Solar-Lezama's website for a deeper dive).
In academia, I serve as an adjunct lecturer at Stanford University in the computer science department. At Stanford, I help master's and PhD students with their research and I teach "Machine Programming" (CS 329M), usually in the autumn quarter each year. Before that, I was an adjunct assistant professor at University of Pennsylvania in the computer science department. Before that I was an adjunct professor at University of Colorado-Boulder in the computer science department (after I completed my PhD there).
I've co-authored a few dozen research papers, have several dozen issued patents, and some number pending. I've been lucky enough to have been invited to give talks at places like Berkeley, BMW, DARPA, IBM Research, MIT, Penn, Stanford, UCLA, University of Washington, VMWare, and Wharton, amongst others. I've had the tremendous honor to give keynote addresses at places like VLDB (LADSIOS), University of Pennsylvania, the US Department of Energy, and MIT. My research has been highlighted in the Wall Street Journal, DeepLearning.ai, Communications of the ACM, MIT Technology Review, The New York Times, and other places.
Former Students
Former Students
MS advisor (University of Pennsylvania): Brad MacDonald -> Tesla
MS co-advisor (University of Pennsylvania): Celine Lee -> Intel Labs, then PhD student @ Cornell
PhD committee member (Lehigh University): PanteA Zardoshti -> Microsoft Research
PhD committee member (University of Washington): Maaz Ahmad -> Adobe Research
MS advisor (University of Pennsylvania): Akhilesh Gupta -> Apple
MS advisor (University of Pennsylvania): Sam Weintraub -> Outrider
PhD committee member (UT-San Antonio): Mohammad Mejbah ul Alam -> Intel Labs, Google
PhD committee member (Lehigh University): Wenjia Ruan -> Qualcomm
PhD co-advisor (Brown University): Irina Calciu -> VMWare Research
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